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Vapor‐pressure mismatched materials such as transition metal chalcogenides have emerged as electronic, photonic, and quantum materials with scientific and technological importance. However, epitaxial growth of vapor‐pressure mismatched materials are challenging due to differences in the reactivity, sticking coefficient, and surface adatom mobility of the mismatched species constituting the material, especially sulfur containing compounds. Here, we report a novel approach to grow chalcogenides – hybrid pulsed laser deposition – wherein an organosulfur precursor is used as a sulfur source in conjunction with pulsed laser deposition to regulate the stoichiometry of the deposited films. Epitaxial or textured thin films of sulfides with variety of structure and chemistry such as alkaline metal chalcogenides, main group chalcogenides, transition metal chalcogenides and chalcogenide perovskites are demonstrated, and structural characterization reveal improvement in thin film crystallinity, and surface and interface roughness compared to the state‐of‐the‐art. The growth method can be broadened to other vapor‐pressure mismatched chalcogenides such as selenides and tellurides. Our work opens up opportunities for broader epitaxial growth of chalcogenides, especially sulfide‐based thin film technological applications.

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Measuring the phenotypic effect of treatments on cells through imaging assays is an efficient and powerful way of studying cell biology, and requires computational methods for transforming images into quantitative data. Here, we present an improved strategy for learning representations of treatment effects from high-throughput imaging, following a causal interpretation. We use weakly supervised learning for modeling associations between images and treatments, and show that it encodes both confounding factors and phenotypic features in the learned representation. To facilitate their separation, we constructed a large training dataset with images from five different studies to maximize experimental diversity, following insights from our causal analysis. Training a model with this dataset successfully improves downstream performance, and produces a reusable convolutional network for image-based profiling, which we call Cell Painting CNN. We evaluated our strategy on three publicly available Cell Painting datasets, and observed that the Cell Painting CNN improves performance in downstream analysis up to 30% with respect to classical features, while also being more computationally efficient.

 

Abstract Predicting assay results for compounds virtually using chemical structures and phenotypic profiles has the potential to reduce the time and resources of screens for drug discovery. Here, we evaluate the relative strength of three high-throughput data sources—chemical structures, imaging (Cell Painting), and gene-expression profiles (L1000)—to predict compound bioactivity using a historical collection of 16,170 compounds tested in 270 assays for a total of 585,439 readouts. All three data modalities can predict compound activity for 6–10% of assays, and in combination they predict 21% of assays with high accuracy, which is a 2 to 3 times higher success rate than using a single modality alone. In practice, the accuracy of predictors could be lower and still be useful, increasing the assays that can be predicted from 37% with chemical structures alone up to 64% when combined with phenotypic data. Our study shows that unbiased phenotypic profiling can be leveraged to enhance compound bioactivity prediction to accelerate the early stages of the drug-discovery process. 

Low-dimensional materials with chain-like (one-dimensional) or layered (two-dimensional) structures are of significant interest due to their anisotropic electrical, optical, and thermal properties. One material with a chain-like structure, BaTiS3 (BTS), was recently shown to possess giant in-plane optical anisotropy and glass-like thermal conductivity. To understand the origin of these effects, it is necessary to fully characterize the optical, thermal, and electronic anisotropy of BTS. To this end, BTS crystals with different orientations (a- and c-axis orientations) were grown by chemical vapor transport. X-ray absorption spectroscopy was used to characterize the local structure and electronic anisotropy of BTS. Fourier transform infrared reflection/transmission spectra show a large in-plane optical anisotropy in the a-oriented crystals, while the c-axis oriented crystals were nearly isotropic in-plane. BTS platelet crystals are promising uniaxial materials for infrared optics with their optic axis parallel to the c-axis. The thermal conductivity measurements revealed a thermal anisotropy of ∼4.5 between the c- and a-axis. Time-domain Brillouin scattering showed that the longitudinal sound speed along the two axes is nearly the same, suggesting that the thermal anisotropy is a result of different phonon scattering rates. 

As one of the most fundamental physical phenomena, charge density wave (CDW) order predominantly occurs in metallic systems such as quasi‐1D metals, doped cuprates, and transition metal dichalcogenides, where it is well understood in terms of Fermi surface nesting and electron–phonon coupling mechanisms. On the other hand, CDW phenomena in semiconducting systems, particularly at the low carrier concentration limit, are less common and feature intricate characteristics, which often necessitate the exploration of novel mechanisms, such as electron–hole coupling or Mott physics, to explain. In this study, an approach combining electrical transport, synchrotron X‐ray diffraction, and density‐functional theory calculations is used to investigate CDW order and a series of hysteretic phase transitions in a diluted‐band semiconductor, BaTiS₃. These experimental and theoretical findings suggest that the observed CDW order and phase transitions in BaTiS₃may be attributed to both electron–phonon coupling and non‐negligible electron–electron interactions in the system. This work highlights BaTiS₃as a unique platform to explore CDW physics and novel electronic phases in the dilute filling limit and opens new opportunities for developing novel electronic devices.

 

Acute lymphoblastic leukemia (ALL) is the most common childhood cancer. While there are a number of well‐recognized prognostic biomarkers at diagnosis, the most powerful independent prognostic factor is the response of the leukemia to induction chemotherapy (Campana and Pui: Blood 129 (2017) 1913–1918). Given the potential for machine learning to improve precision medicine, we tested its capacity to monitor disease in children undergoing ALL treatment. Diagnostic and on‐treatment bone marrow samples were labeled with an ALL‐discriminating antibody combination and analyzed by imaging flow cytometry. Ignoring the fluorescent markers and using only features extracted from bright‐field and dark‐field cell images, a deep learning model was able to identify ALL cells at an accuracy of >88%. This antibody‐free, single cell method is cheap, quick, and could be adapted to a simple, laser‐free cytometer to allow automated, point‐of‐care testing to detect slow early responders. Adaptation to other types of leukemia is feasible, which would revolutionize residual disease monitoring. © 2020 The Authors.Cytometry Part Apublished by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.